It is a common practice in today's microelectronics manufacturing facilities to have many different products and processes run on each processing tool. This is caused mainly by the high capital costs associated with the tools and the limited capacity of the facility. A run-to-run controller relies on having a model that is consistent from run to run. When the different processes run on the tool are significantly different, the controller may behave unexpectedly because each change to a new process can appear as a large disturbance. In addition, it may take several successive runs of a given process for the controller to stabilize, but this cannot happen if the processes change too often. Ideally, the controller should be able to determine optimal settings for all processes that must run on the tool, regardless of the order in which they appear. In an adaptive control strategy, an online system identification scheme runs along with the controller and constantly adjusts the model so that it mimics the true behavior of the system. One very difficult task in this situation is determining whether observed errors in the output are due to errors in accounting for tool differences or for product differences. This discussion will outline a scheme for deciding which model parameters are in error and performing the correct model updates.